Multi-objective Optimization of Land Use Allocation Using NSGA-II Algorithm (Case Study: Region 10 of Tabriz City)

Document Type : Research Paper

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Abstract

Optimization of land use allocation as one of the best models for environmental protection and coordinating different economic, social and other land use goals. The 10th Region of Tabriz has been inadequate in terms of fitting and optimal distribution of applications. Therefore, this study aimed to optimize land use allocation in 10th Region of Tabriz metropolitan. The present research deals with the nature of developmental-applied and descriptive-analytical method in the allocating land use field using eight main uses: housing divided into three classes, commercial, educational, therapeutic, cultural, green spaces, sports Commercial-residential, categorized by three types of classes, is based on the multi-objective genetic optimization algorithm, the second-class non-occupational sorting (NSGA-II). To this end, four maximum nonlinear objective functions are used: maximizing FAR, maximizing economic profit, maximizing adaptability, compression maximization. Eight types of land use and eight limitations were defined, and the combined weight method was used to combine the numerical value of the objective function after normalization. The NSGA-II operators were modified and the proposed model was developed in the MATLAB programming language. The model was implemented according to the modeling constraints. To illustrate the output of the model, the special coding method for solutions that defined the output was defined by the GIS software. The results indicate the effectiveness and effectiveness of the proposed model and its potential in supporting the urban planning and decision-making process. This potential is achieved through the production of alternative alternatives and the display of optimal solutions. The results of this study showed that in the case of optimization of land use in the 10th district of Tabriz, the FAR value is 13.04% and economic profit is 21.06% and compatibility-compression between applications is 2.3 and 4.6%, respectively.--

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References

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Geography and Territorial Spatial Arrangement
Volume 10, Issue 35
June 2020
Pages 163-190

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  • Receive Date: 25 August 2020
  • Accept Date: 25 August 2020

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